Methods for ascertaining a control strategy for a technical system using a Bayesian optimization method. The control strategy is created based on model parameters of a control model and is executable. The method includes providing a quality function whose shape corresponds to a regression function and that evaluates a quality of a controlling of the technical system based on model parameters; carrying out a Bayesian optimization method based on the quality function in order to iteratively ascertain a model parameter set having model parameters within a model parameter domain that indicates the permissible value ranges for the model parameters; and determining the model parameter domain for at least one of the model parameters as a function of an associated maximum a posteriori estimated value of the quality function.
Legal claims defining the scope of protection, as filed with the USPTO.
2. The method as recited in claim 1, wherein a parametric regression model maps an input variable vector and a system state of the technical system onto a subsequent system state, and being correspondingly trained in order to obtain a weighting matrix.
3. The method as recited in claim 1, wherein the model parameters are ascertained using an AB learning method for a linear quadratic regulator (LQR) controller, an uncertainty measure being determined in each case for the at least one maximum a posteriori estimated value, the value range of the at least one model parameter being defined around the maximum a posteriori estimated value.
4. The method as recited in claim 3, wherein the value range of the at least one model parameter being determined around the maximum a posteriori estimated value with specification of an uncertainty of an expected value.
5. The method as recited in claim 1, wherein the model parameters are ascertained using a K-Learning method for a linear quadratic regulator (LQR) controller, the value range of the at least one model parameter being defined around the maximum a posteriori estimated value.
6. The method as recited in claim 5, wherein the value range of the at least one model parameter is determined around the maximum a posteriori estimated value with a measure that is determined as a product of a specified factor between 0 and 1 of the maximum a posteriori estimated value.
7. The method as recited in claim 1, wherein the optimization method is started with initial model parameters that result from a minimization of a prior mean value function, a non-parametric approximation model of the technical system being trained in order to obtain the prior mean value function.
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May 27, 2020
December 3, 2024
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